import numpy as np
import pandas as pd
import time
from datetime import datetime, timedelta
import logging
from enum import Enum
from typing import Dict, Any, Optional, List, Tuple, Callable

# 配置日志
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
logger = logging.getLogger('ExecutionStrategy')

class ExecutionType(Enum):
    """执行策略类型枚举"""
    MARKET = 1          # 市价单
    LIMIT = 2           # 限价单
    STOP = 3            # 止损单
    STOP_LIMIT = 4      # 止损限价单
    VWAP = 5            # 成交量加权平均价
    TWAP = 6            # 时间加权平均价
    ICEBERG = 7         # 冰山订单
    POV = 8             # 成交量参与率
    PEGGED = 9          # 钉住订单
    SNIFFER = 10        # 嗅探订单
    DARK = 11           # 暗池交易
    ALGO_ADAPTIVE = 12  # 自适应算法

class TimeInForce(Enum):
    """订单有效期枚举"""
    DAY = 1             # 当日有效
    GTC = 2             # 撤销前有效
    IOC = 3             # 立即成交否则取消
    FOK = 4             # 全部成交否则取消
    GTX = 5             # 无法成交部分转到下一交易日

class ExecutionStrategy:
    """执行策略模块，负责不同执行策略的管理和选择"""
    def __init__(self, config=None):
        self.config = config or {
            'default_execution_type': ExecutionType.MARKET,
            'default_time_in_force': TimeInForce.DAY,
            'min_execution_window_seconds': 60,
            'max_execution_window_seconds': 3600,
            'default_vwap_window_minutes': 5,
            'default_twap_window_minutes': 5,
            'iceberg_display_ratio': 0.1,
            'default_pov_rate': 0.05,
            'slippage_tolerance': 0.005,
            'market_impact_limit': 0.01,
            'retry_attempts': 3,
            'retry_delay_seconds': 2,
            'debug_mode': False
        }
        
        # 执行策略注册表
        self.strategy_registry = {
            ExecutionType.MARKET: self._execute_market_order,
            ExecutionType.LIMIT: self._execute_limit_order,
            ExecutionType.STOP: self._execute_stop_order,
            ExecutionType.STOP_LIMIT: self._execute_stop_limit_order,
            ExecutionType.VWAP: self._execute_vwap_order,
            ExecutionType.TWAP: self._execute_twap_order,
            ExecutionType.ICEBERG: self._execute_iceberg_order,
            ExecutionType.POV: self._execute_pov_order,
            ExecutionType.PEGGED: self._execute_pegged_order,
            ExecutionType.SNIFFER: self._execute_sniffer_order,
            ExecutionType.DARK: self._execute_dark_order,
            ExecutionType.ALGO_ADAPTIVE: self._execute_adaptive_order
        }
        
        # 执行历史
        self.execution_history = []
        
        # 性能统计
        self.performance_stats = {
            'total_executions': 0,
            'successful_executions': 0,
            'failed_executions': 0,
            'avg_execution_time_ms': 0,
            'avg_slippage': 0,
            'avg_market_impact': 0,
            'strategy_usage_count': {}
        }
        
        # 初始化策略使用计数
        for strategy_type in ExecutionType:
            self.performance_stats['strategy_usage_count'][strategy_type.name] = 0
        
        # 市场数据提供者（实际应用中应该注入）
        self.market_data_provider = None
    
    def set_market_data_provider(self, provider):
        """设置市场数据提供者"""
        self.market_data_provider = provider
    
    def select_optimal_strategy(self, symbol: str, qty: int, order_type: str, market_data: Dict[str, Any]) -> ExecutionType:
        """根据市场条件选择最优执行策略"""
        try:
            # 获取市场数据
            if self.market_data_provider:
                current_market_data = self.market_data_provider.get_market_data(symbol)
                market_data.update(current_market_data)
            
            # 提取关键市场指标
            liquidity_score = self._calculate_liquidity_score(symbol, market_data)
            volatility = self._calculate_volatility(symbol, market_data)
            order_size_ratio = self._calculate_order_size_ratio(symbol, qty, market_data)
            
            # 根据订单类型和市场条件选择策略
            if order_type == 'MARKET':
                # 市价单的策略选择
                if order_size_ratio > 0.1:  # 大订单
                    if liquidity_score > 0.7:  # 高流动性
                        return ExecutionType.VWAP  # 使用VWAP减少市场冲击
                    else:  # 低流动性
                        return ExecutionType.ICEBERG  # 使用冰山订单隐藏大单
                else:  # 小订单
                    return ExecutionType.MARKET  # 直接市价单
                    
            elif order_type == 'LIMIT':
                # 限价单的策略选择
                if volatility > 0.02:  # 高波动率
                    return ExecutionType.PEGGED  # 使用钉住订单跟随市场价格
                else:  # 低波动率
                    return ExecutionType.LIMIT  # 标准限价单
                    
            elif order_type == 'STOP':
                # 止损单的策略选择
                return ExecutionType.STOP_LIMIT  # 止损限价单可以提供更好的价格控制
                
            # 默认返回市场订单
            return self.config['default_execution_type']
            
        except Exception as e:
            logger.error(f"Error selecting optimal strategy for {symbol}: {str(e)}")
            # 出错时返回默认策略
            return self.config['default_execution_type']
    
    def _calculate_liquidity_score(self, symbol: str, market_data: Dict[str, Any]) -> float:
        """计算流动性评分（0-1）"""
        try:
            # 提取市场数据中的流动性指标
            bid_ask_spread = market_data.get('bid_ask_spread', 0.001)
            avg_daily_volume = market_data.get('avg_daily_volume', 1000000)
            market_depth = market_data.get('market_depth', 100000)
            
            # 计算买卖价差评分（价差越小，流动性越好）
            spread_score = max(0.0, min(1.0, 1.0 - (bid_ask_spread / 0.01)))  # 假设0.01是最大价差
            
            # 计算成交量评分（成交量越大，流动性越好）
            volume_score = min(1.0, avg_daily_volume / 1000000)  # 假设100万是高成交量
            
            # 计算市场深度评分（深度越大，流动性越好）
            depth_score = min(1.0, market_depth / 100000)  # 假设10万是高深度
            
            # 综合流动性评分
            liquidity_score = (spread_score * 0.4 + volume_score * 0.3 + depth_score * 0.3)
            
            return liquidity_score
            
        except Exception as e:
            logger.error(f"Error calculating liquidity score for {symbol}: {str(e)}")
            return 0.5  # 默认中等流动性
    
    def _calculate_volatility(self, symbol: str, market_data: Dict[str, Any]) -> float:
        """计算波动率"""
        try:
            # 从市场数据获取波动率
            if 'volatility' in market_data:
                return market_data['volatility']
                
            # 从价格历史计算波动率
            prices = market_data.get('price_history', [])
            
            if len(prices) >= 20:
                # 计算日收益率
                returns = np.diff(prices) / prices[:-1]
                # 计算年化波动率
                daily_volatility = np.std(returns)
                annualized_volatility = daily_volatility * np.sqrt(252)
                return annualized_volatility
            
            # 默认返回中等波动率
            return 0.2  # 20%年化波动率
            
        except Exception as e:
            logger.error(f"Error calculating volatility for {symbol}: {str(e)}")
            return 0.2  # 默认中等波动率
    
    def _calculate_order_size_ratio(self, symbol: str, qty: int, market_data: Dict[str, Any]) -> float:
        """计算订单大小比例（订单量/平均日成交量）"""
        try:
            avg_daily_volume = market_data.get('avg_daily_volume', 1000000)
            
            if avg_daily_volume > 0:
                return min(1.0, qty / avg_daily_volume)
            
            return 0.0
            
        except Exception as e:
            logger.error(f"Error calculating order size ratio for {symbol}: {str(e)}")
            return 0.0
    
    def execute(self, strategy_type: ExecutionType, order_params: Dict[str, Any]) -> Dict[str, Any]:
        """执行指定策略"""
        # 记录开始时间
        start_time = time.time()
        
        try:
            # 检查策略类型是否支持
            if strategy_type not in self.strategy_registry:
                return {
                    'success': False,
                    'error': f'Unsupported execution strategy: {strategy_type.name}',
                    'execution_time_ms': (time.time() - start_time) * 1000
                }
            
            # 获取策略执行函数
            strategy_func = self.strategy_registry[strategy_type]
            
            # 执行策略
            result = strategy_func(order_params)
            
            # 记录执行历史
            execution_record = {
                'timestamp': datetime.now().timestamp(),
                'strategy_type': strategy_type.name,
                'order_params': order_params,
                'result': result,
                'execution_time_ms': (time.time() - start_time) * 1000
            }
            
            self.execution_history.append(execution_record)
            
            # 限制历史记录大小
            max_history_size = 1000
            if len(self.execution_history) > max_history_size:
                self.execution_history.pop(0)
            
            # 更新性能统计
            self._update_performance_stats(strategy_type, result, (time.time() - start_time) * 1000)
            
            return result
            
        except Exception as e:
            logger.error(f"Error executing strategy {strategy_type.name}: {str(e)}")
            
            # 记录失败执行
            execution_record = {
                'timestamp': datetime.now().timestamp(),
                'strategy_type': strategy_type.name,
                'order_params': order_params,
                'result': {'success': False, 'error': str(e)},
                'execution_time_ms': (time.time() - start_time) * 1000
            }
            
            self.execution_history.append(execution_record)
            
            # 更新性能统计
            self._update_performance_stats(strategy_type, {'success': False, 'error': str(e)}, (time.time() - start_time) * 1000)
            
            return {
                'success': False,
                'error': str(e),
                'execution_time_ms': (time.time() - start_time) * 1000
            }
    
    def _execute_market_order(self, order_params: Dict[str, Any]) -> Dict[str, Any]:
        """执行市价单"""
        # 模拟市价单执行
        # 实际应用中，这里应该调用订单路由系统发送订单
        symbol = order_params.get('symbol', '')
        qty = order_params.get('qty', 0)
        side = order_params.get('side', 'BUY')
        
        # 模拟成交
        execution_price = order_params.get('current_price', 0.0)
        
        if execution_price <= 0:
            # 如果没有提供价格，生成一个随机价格
            execution_price = random.uniform(100, 200)
        
        # 模拟滑点（市价单通常有一定滑点）
        slippage = execution_price * self.config['slippage_tolerance'] * random.random()
        
        if side == 'BUY':
            avg_fill_price = execution_price + slippage
        else:
            avg_fill_price = execution_price - slippage
        
        # 模拟成交数量（假设100%成交）
        filled_qty = qty
        
        return {
            'success': True,
            'order_id': str(int(time.time() * 1000)),  # 生成简单的订单ID
            'symbol': symbol,
            'side': side,
            'qty': qty,
            'filled_qty': filled_qty,
            'avg_fill_price': avg_fill_price,
            'execution_type': 'MARKET',
            'slippage': slippage,
            'market_impact': slippage / execution_price,
            'message': 'Market order executed successfully'
        }
    
    def _execute_limit_order(self, order_params: Dict[str, Any]) -> Dict[str, Any]:
        """执行限价单"""
        # 模拟限价单执行
        symbol = order_params.get('symbol', '')
        qty = order_params.get('qty', 0)
        side = order_params.get('side', 'BUY')
        limit_price = order_params.get('limit_price', 0.0)
        time_in_force = order_params.get('time_in_force', self.config['default_time_in_force'])
        
        if limit_price <= 0:
            return {'success': False, 'error': 'Limit price must be positive'}
        
        # 获取当前市场价格
        current_price = order_params.get('current_price', 0.0)
        
        if current_price <= 0:
            # 如果没有提供价格，生成一个随机价格
            current_price = random.uniform(100, 200)
        
        # 检查限价单是否可以立即成交
        if (side == 'BUY' and limit_price >= current_price) or 
           (side == 'SELL' and limit_price <= current_price):
            # 可以立即成交
            avg_fill_price = min(limit_price, current_price) if side == 'BUY' else max(limit_price, current_price)
            filled_qty = qty
            status = 'FILLED'
        else:
            # 无法立即成交，部分成交或挂单
            # 模拟部分成交（50%的概率）
            if random.random() < 0.5:
                filled_qty = int(qty * random.uniform(0.1, 0.9))
                avg_fill_price = limit_price
                status = 'PARTIALLY_FILLED'
            else:
                filled_qty = 0
                avg_fill_price = 0.0
                status = 'PENDING'
        
        return {
            'success': status in ['FILLED', 'PARTIALLY_FILLED', 'PENDING'],
            'order_id': str(int(time.time() * 1000)),
            'symbol': symbol,
            'side': side,
            'qty': qty,
            'limit_price': limit_price,
            'filled_qty': filled_qty,
            'avg_fill_price': avg_fill_price,
            'status': status,
            'time_in_force': time_in_force.name,
            'execution_type': 'LIMIT',
            'message': f'Limit order executed with status: {status}'
        }
    
    def _execute_stop_order(self, order_params: Dict[str, Any]) -> Dict[str, Any]:
        """执行止损单"""
        # 模拟止损单执行
        symbol = order_params.get('symbol', '')
        qty = order_params.get('qty', 0)
        side = order_params.get('side', 'BUY')
        stop_price = order_params.get('stop_price', 0.0)
        
        if stop_price <= 0:
            return {'success': False, 'error': 'Stop price must be positive'}
        
        # 获取当前市场价格
        current_price = order_params.get('current_price', 0.0)
        
        if current_price <= 0:
            # 如果没有提供价格，生成一个随机价格
            current_price = random.uniform(100, 200)
        
        # 检查止损单是否触发
        if (side == 'BUY' and current_price >= stop_price) or 
           (side == 'SELL' and current_price <= stop_price):
            # 触发止损，转为市价单
            # 模拟滑点
            slippage = current_price * self.config['slippage_tolerance'] * random.random()
            
            if side == 'BUY':
                avg_fill_price = current_price + slippage
            else:
                avg_fill_price = current_price - slippage
            
            filled_qty = qty
            status = 'FILLED'
        else:
            # 未触发
            filled_qty = 0
            avg_fill_price = 0.0
            status = 'PENDING'
        
        return {
            'success': status in ['FILLED', 'PENDING'],
            'order_id': str(int(time.time() * 1000)),
            'symbol': symbol,
            'side': side,
            'qty': qty,
            'stop_price': stop_price,
            'filled_qty': filled_qty,
            'avg_fill_price': avg_fill_price,
            'status': status,
            'execution_type': 'STOP',
            'message': f'Stop order executed with status: {status}'
        }
    
    def _execute_stop_limit_order(self, order_params: Dict[str, Any]) -> Dict[str, Any]:
        """执行止损限价单"""
        # 模拟止损限价单执行
        symbol = order_params.get('symbol', '')
        qty = order_params.get('qty', 0)
        side = order_params.get('side', 'BUY')
        stop_price = order_params.get('stop_price', 0.0)
        limit_price = order_params.get('limit_price', 0.0)
        
        if stop_price <= 0 or limit_price <= 0:
            return {'success': False, 'error': 'Stop price and limit price must be positive'}
        
        # 获取当前市场价格
        current_price = order_params.get('current_price', 0.0)
        
        if current_price <= 0:
            # 如果没有提供价格，生成一个随机价格
            current_price = random.uniform(100, 200)
        
        # 检查止损单是否触发
        if (side == 'BUY' and current_price >= stop_price) or 
           (side == 'SELL' and current_price <= stop_price):
            # 触发止损，转为限价单
            # 检查限价是否可以成交
            if (side == 'BUY' and limit_price >= current_price) or 
               (side == 'SELL' and limit_price <= current_price):
                # 可以成交
                avg_fill_price = min(limit_price, current_price) if side == 'BUY' else max(limit_price, current_price)
                filled_qty = qty
                status = 'FILLED'
            else:
                # 无法立即成交
                filled_qty = 0
                avg_fill_price = 0.0
                status = 'PENDING'
        else:
            # 未触发
            filled_qty = 0
            avg_fill_price = 0.0
            status = 'PENDING'
        
        return {
            'success': status in ['FILLED', 'PENDING'],
            'order_id': str(int(time.time() * 1000)),
            'symbol': symbol,
            'side': side,
            'qty': qty,
            'stop_price': stop_price,
            'limit_price': limit_price,
            'filled_qty': filled_qty,
            'avg_fill_price': avg_fill_price,
            'status': status,
            'execution_type': 'STOP_LIMIT',
            'message': f'Stop-limit order executed with status: {status}'
        }
    
    def _execute_vwap_order(self, order_params: Dict[str, Any]) -> Dict[str, Any]:
        """执行VWAP（成交量加权平均价）订单"""
        # 模拟VWAP执行
        symbol = order_params.get('symbol', '')
        qty = order_params.get('qty', 0)
        side = order_params.get('side', 'BUY')
        window_minutes = order_params.get('window_minutes', self.config['default_vwap_window_minutes'])
        
        # 模拟执行过程
        # 实际应用中，应该根据实时成交量分布动态调整下单节奏
        
        # 模拟分10个时间段执行
        intervals = 10
        interval_time = (window_minutes * 60) / intervals  # 秒
        
        total_filled = 0
        total_value = 0
        
        # 获取初始价格
        current_price = order_params.get('current_price', 0.0)
        
        if current_price <= 0:
            # 如果没有提供价格，生成一个随机价格
            current_price = random.uniform(100, 200)
        
        for i in range(intervals):
            # 模拟每个时间段的成交量权重（通常中间时间段成交量较大）
            if 3 <= i <= 7:
                weight = random.uniform(0.12, 0.15)
            else:
                weight = random.uniform(0.05, 0.1)
            
            # 计算该时间段的下单量
            interval_qty = int(qty * weight)
            
            # 模拟价格变化
            price_change = random.uniform(-0.001, 0.001) * i  # 模拟价格随时间变化
            execution_price = current_price * (1 + price_change)
            
            # 模拟滑点
            slippage = execution_price * self.config['slippage_tolerance'] * random.random()
            
            if side == 'BUY':
                fill_price = execution_price + slippage
            else:
                fill_price = execution_price - slippage
            
            # 更新成交信息
            total_filled += interval_qty
            total_value += interval_qty * fill_price
            
            # 模拟时间延迟
            if self.config['debug_mode']:
                time.sleep(interval_time * 0.01)  # 调试模式下加速
            else:
                time.sleep(interval_time)
        
        # 计算平均成交价
        avg_fill_price = total_value / total_filled if total_filled > 0 else 0.0
        
        return {
            'success': True,
            'order_id': str(int(time.time() * 1000)),
            'symbol': symbol,
            'side': side,
            'qty': qty,
            'filled_qty': total_filled,
            'avg_fill_price': avg_fill_price,
            'execution_type': 'VWAP',
            'window_minutes': window_minutes,
            'message': f'VWAP order executed over {window_minutes} minutes'
        }
    
    def _execute_twap_order(self, order_params: Dict[str, Any]) -> Dict[str, Any]:
        """执行TWAP（时间加权平均价）订单"""
        # 模拟TWAP执行
        symbol = order_params.get('symbol', '')
        qty = order_params.get('qty', 0)
        side = order_params.get('side', 'BUY')
        window_minutes = order_params.get('window_minutes', self.config['default_twap_window_minutes'])
        
        # 模拟执行过程
        
        # 模拟分10个时间段均匀执行
        intervals = 10
        interval_qty = qty // intervals
        remainder = qty % intervals
        interval_time = (window_minutes * 60) / intervals  # 秒
        
        total_filled = 0
        total_value = 0
        
        # 获取初始价格
        current_price = order_params.get('current_price', 0.0)
        
        if current_price <= 0:
            # 如果没有提供价格，生成一个随机价格
            current_price = random.uniform(100, 200)
        
        for i in range(intervals):
            # 计算该时间段的下单量
            current_qty = interval_qty + (1 if i < remainder else 0)
            
            # 模拟价格变化
            price_change = random.uniform(-0.001, 0.001) * i  # 模拟价格随时间变化
            execution_price = current_price * (1 + price_change)
            
            # 模拟滑点
            slippage = execution_price * self.config['slippage_tolerance'] * random.random()
            
            if side == 'BUY':
                fill_price = execution_price + slippage
            else:
                fill_price = execution_price - slippage
            
            # 更新成交信息
            total_filled += current_qty
            total_value += current_qty * fill_price
            
            # 模拟时间延迟
            if self.config['debug_mode']:
                time.sleep(interval_time * 0.01)  # 调试模式下加速
            else:
                time.sleep(interval_time)
        
        # 计算平均成交价
        avg_fill_price = total_value / total_filled if total_filled > 0 else 0.0
        
        return {
            'success': True,
            'order_id': str(int(time.time() * 1000)),
            'symbol': symbol,
            'side': side,
            'qty': qty,
            'filled_qty': total_filled,
            'avg_fill_price': avg_fill_price,
            'execution_type': 'TWAP',
            'window_minutes': window_minutes,
            'message': f'TWAP order executed over {window_minutes} minutes'
        }
    
    def _execute_iceberg_order(self, order_params: Dict[str, Any]) -> Dict[str, Any]:
        """执行冰山订单（隐藏大部分订单量）"""
        # 模拟冰山订单执行
        symbol = order_params.get('symbol', '')
        qty = order_params.get('qty', 0)
        side = order_params.get('side', 'BUY')
        limit_price = order_params.get('limit_price', 0.0)
        display_ratio = order_params.get('display_ratio', self.config['iceberg_display_ratio'])
        
        if limit_price <= 0:
            return {'success': False, 'error': 'Limit price must be positive'}
        
        # 计算显示和隐藏的订单量
        display_size = max(1, int(qty * display_ratio))
        
        # 模拟执行
        total_filled = 0
        total_value = 0
        
        # 模拟分多次显示小订单
        while total_filled < qty:
            # 计算本次显示的订单量
            current_display = min(display_size, qty - total_filled)
            
            # 模拟部分成交
            fill_rate = random.uniform(0.8, 1.0)
            filled = int(current_display * fill_rate)
            
            # 更新成交信息
            total_filled += filled
            total_value += filled * limit_price
            
            # 如果还有剩余量，继续显示
            if total_filled < qty:
                # 等待一段时间后再次显示
                time.sleep(random.uniform(0.5, 2.0))
        
        # 计算平均成交价
        avg_fill_price = total_value / total_filled if total_filled > 0 else 0.0
        
        return {
            'success': True,
            'order_id': str(int(time.time() * 1000)),
            'symbol': symbol,
            'side': side,
            'qty': qty,
            'limit_price': limit_price,
            'filled_qty': total_filled,
            'avg_fill_price': avg_fill_price,
            'execution_type': 'ICEBERG',
            'display_ratio': display_ratio,
            'message': 'Iceberg order executed successfully'
        }
    
    def _execute_pov_order(self, order_params: Dict[str, Any]) -> Dict[str, Any]:
        """执行POV（成交量参与率）订单"""
        # 模拟POV执行
        symbol = order_params.get('symbol', '')
        qty = order_params.get('qty', 0)
        side = order_params.get('side', 'BUY')
        participation_rate = order_params.get('participation_rate', self.config['default_pov_rate'])
        
        # 模拟执行
        total_filled = 0
        total_value = 0
        
        # 获取初始价格
        current_price = order_params.get('current_price', 0.0)
        
        if current_price <= 0:
            # 如果没有提供价格，生成一个随机价格
            current_price = random.uniform(100, 200)
        
        # 模拟执行时间窗口（5分钟）
        execution_window_seconds = 300  # 5分钟
        interval_seconds = 10  # 每10秒检查一次
        intervals = execution_window_seconds // interval_seconds
        
        for i in range(intervals):
            # 模拟市场成交量（随机波动）
            market_volume = random.uniform(10000, 50000)
            
            # 计算本时间段的下单量
            interval_volume = market_volume * participation_rate * (interval_seconds / 60)  # 转换为秒级成交量
            current_qty = min(int(interval_volume), qty - total_filled)
            
            if current_qty <= 0:
                break
            
            # 模拟价格变化
            price_change = random.uniform(-0.001, 0.001)  # 模拟价格短期波动
            execution_price = current_price * (1 + price_change)
            
            # 模拟滑点
            slippage = execution_price * self.config['slippage_tolerance'] * random.random()
            
            if side == 'BUY':
                fill_price = execution_price + slippage
            else:
                fill_price = execution_price - slippage
            
            # 更新成交信息
            total_filled += current_qty
            total_value += current_qty * fill_price
            
            # 模拟时间延迟
            if self.config['debug_mode']:
                time.sleep(0.01)  # 调试模式下加速
            else:
                time.sleep(interval_seconds)
        
        # 计算平均成交价
        avg_fill_price = total_value / total_filled if total_filled > 0 else 0.0
        
        return {
            'success': True,
            'order_id': str(int(time.time() * 1000)),
            'symbol': symbol,
            'side': side,
            'qty': qty,
            'filled_qty': total_filled,
            'avg_fill_price': avg_fill_price,
            'execution_type': 'POV',
            'participation_rate': participation_rate,
            'message': f'POV order executed with participation rate {participation_rate*100}%'
        }
    
    def _execute_pegged_order(self, order_params: Dict[str, Any]) -> Dict[str, Any]:
        """执行钉住订单"""
        # 模拟钉住订单执行
        symbol = order_params.get('symbol', '')
        qty = order_params.get('qty', 0)
        side = order_params.get('side', 'BUY')
        peg_type = order_params.get('peg_type', 'BEST_BID_ASK')
        offset = order_params.get('offset', 0.0)
        
        # 模拟执行
        total_filled = 0
        total_value = 0
        
        # 模拟分3次尝试钉住价格
        for attempt in range(3):
            # 获取当前市场价格
            current_price = order_params.get('current_price', 0.0)
            
            if current_price <= 0:
                # 如果没有提供价格，生成一个随机价格
                current_price = random.uniform(100, 200)
            
            # 根据钉住类型计算订单价格
            if peg_type == 'BEST_BID_ASK':
                if side == 'BUY':
                    peg_price = current_price + offset
                else:
                    peg_price = current_price - offset
            elif peg_type == 'MARKET_CENTER_BEST':
                # 简化处理，假设与最佳买卖价相同
                if side == 'BUY':
                    peg_price = current_price + offset
                else:
                    peg_price = current_price - offset
            else:
                # 默认钉住当前价格
                peg_price = current_price
            
            # 模拟部分成交
            fill_rate = random.uniform(0.3, 0.7)
            filled = int(qty * fill_rate)
            
            # 更新成交信息
            total_filled += filled
            total_value += filled * peg_price
            
            # 如果已完成，退出循环
            if total_filled >= qty:
                break
            
            # 等待一段时间后再次尝试
            time.sleep(random.uniform(1.0, 3.0))
        
        # 计算平均成交价
        avg_fill_price = total_value / total_filled if total_filled > 0 else 0.0
        
        return {
            'success': True,
            'order_id': str(int(time.time() * 1000)),
            'symbol': symbol,
            'side': side,
            'qty': qty,
            'filled_qty': total_filled,
            'avg_fill_price': avg_fill_price,
            'execution_type': 'PEGGED',
            'peg_type': peg_type,
            'message': f'Pegged order executed with type: {peg_type}'
        }
    
    def _execute_sniffer_order(self, order_params: Dict[str, Any]) -> Dict[str, Any]:
        """执行嗅探订单（寻找最优价格）"""
        # 模拟嗅探订单执行
        symbol = order_params.get('symbol', '')
        qty = order_params.get('qty', 0)
        side = order_params.get('side', 'BUY')
        
        # 模拟执行
        total_filled = 0
        total_value = 0
        best_price = float('inf') if side == 'BUY' else 0.0
        
        # 模拟在多个交易场所寻找最优价格
        venues = ['Exchange_A', 'Exchange_B', 'Exchange_C', 'Exchange_D', 'Exchange_E']
        
        for venue in venues:
            # 为每个交易场所生成不同的价格
            if side == 'BUY':
                # 买方向，寻找最低价格
                venue_price = random.uniform(95, 105)
                if venue_price < best_price:
                    best_price = venue_price
                    # 在最佳价格的交易场所执行部分订单
                    filled = int(qty * random.uniform(0.1, 0.4))
                    total_filled += filled
                    total_value += filled * best_price
            else:
                # 卖方向，寻找最高价格
                venue_price = random.uniform(95, 105)
                if venue_price > best_price:
                    best_price = venue_price
                    # 在最佳价格的交易场所执行部分订单
                    filled = int(qty * random.uniform(0.1, 0.4))
                    total_filled += filled
                    total_value += filled * best_price
            
            # 如果已完成，退出循环
            if total_filled >= qty:
                break
            
            # 模拟在不同交易场所之间的延迟
            time.sleep(0.1)
        
        # 计算平均成交价
        avg_fill_price = total_value / total_filled if total_filled > 0 else 0.0
        
        return {
            'success': True,
            'order_id': str(int(time.time() * 1000)),
            'symbol': symbol,
            'side': side,
            'qty': qty,
            'filled_qty': total_filled,
            'avg_fill_price': avg_fill_price,
            'execution_type': 'SNIFFER',
            'best_venue': venues[0] if venues else 'N/A',  # 简化处理
            'message': 'Sniffer order executed across multiple venues'
        }
    
    def _execute_dark_order(self, order_params: Dict[str, Any]) -> Dict[str, Any]:
        """执行暗池交易订单"""
        # 模拟暗池交易执行
        symbol = order_params.get('symbol', '')
        qty = order_params.get('qty', 0)
        side = order_params.get('side', 'BUY')
        
        # 暗池交易通常有一定的成交概率
        fill_probability = random.uniform(0.3, 0.8)  # 30%-80%的成交概率
        
        if random.random() < fill_probability:
            # 成交
            # 获取当前市场价格
            current_price = order_params.get('current_price', 0.0)
            
            if current_price <= 0:
                # 如果没有提供价格，生成一个随机价格
                current_price = random.uniform(100, 200)
            
            # 暗池交易通常有更优的价格（比公开市场好1-2个基点）
            if side == 'BUY':
                dark_price = current_price * (1 - random.uniform(0.0001, 0.0002))
            else:
                dark_price = current_price * (1 + random.uniform(0.0001, 0.0002))
            
            # 模拟部分成交
            fill_rate = random.uniform(0.5, 1.0)
            filled = int(qty * fill_rate)
            
            return {
                'success': True,
                'order_id': str(int(time.time() * 1000)),
                'symbol': symbol,
                'side': side,
                'qty': qty,
                'filled_qty': filled,
                'avg_fill_price': dark_price,
                'execution_type': 'DARK',
                'dark_pool': 'DarkPool_XYZ',  # 简化处理
                'message': 'Dark pool order executed successfully'
            }
        else:
            # 未成交
            return {
                'success': True,
                'order_id': str(int(time.time() * 1000)),
                'symbol': symbol,
                'side': side,
                'qty': qty,
                'filled_qty': 0,
                'avg_fill_price': 0.0,
                'execution_type': 'DARK',
                'status': 'PENDING',
                'message': 'Dark pool order pending, no liquidity found'
            }
    
    def _execute_adaptive_order(self, order_params: Dict[str, Any]) -> Dict[str, Any]:
        """执行自适应算法订单"""
        # 模拟自适应算法执行
        # 自适应算法会根据市场条件动态调整执行策略
        symbol = order_params.get('symbol', '')
        qty = order_params.get('qty', 0)
        
        # 首先选择初始策略
        initial_strategy = self.select_optimal_strategy(symbol, qty, 'MARKET', order_params)
        
        # 执行初始策略
        result = self.execute(initial_strategy, order_params)
        
        # 检查执行结果，必要时调整策略
        if result.get('filled_qty', 0) < qty * 0.5:  # 如果成交量不足50%
            # 尝试切换到另一种策略
            if initial_strategy == ExecutionType.VWAP:
                # 从VWAP切换到TWAP
                second_strategy = ExecutionType.TWAP
            elif initial_strategy == ExecutionType.TWAP:
                # 从TWAP切换到ICEBERG
                second_strategy = ExecutionType.ICEBERG
            else:
                # 其他情况切换到市价单
                second_strategy = ExecutionType.MARKET
            
            # 更新剩余数量
            remaining_qty = qty - result.get('filled_qty', 0)
            order_params['qty'] = remaining_qty
            
            # 执行第二种策略
            second_result = self.execute(second_strategy, order_params)
            
            # 合并结果
            result['filled_qty'] += second_result.get('filled_qty', 0)
            result['avg_fill_price'] = (
                (result.get('filled_qty', 0) * result.get('avg_fill_price', 0) +
                 second_result.get('filled_qty', 0) * second_result.get('avg_fill_price', 0)) /
                (result.get('filled_qty', 0) + second_result.get('filled_qty', 0))
            ) if (result.get('filled_qty', 0) + second_result.get('filled_qty', 0)) > 0 else 0
            
            result['adaptive_strategies'] = [initial_strategy.name, second_strategy.name]
            result['message'] = f'Adaptive order executed with strategies: {initial_strategy.name}, {second_strategy.name}'
        else:
            result['adaptive_strategies'] = [initial_strategy.name]
            result['message'] = f'Adaptive order executed with strategy: {initial_strategy.name}'
        
        return result
    
    def _update_performance_stats(self, strategy_type: ExecutionType, result: Dict[str, Any], execution_time_ms: float):
        """更新性能统计"""
        # 更新总执行次数
        self.performance_stats['total_executions'] += 1
        
        # 更新策略使用计数
        self.performance_stats['strategy_usage_count'][strategy_type.name] += 1
        
        # 更新成功/失败统计
        if result.get('success', False):
            self.performance_stats['successful_executions'] += 1
        else:
            self.performance_stats['failed_executions'] += 1
        
        # 更新平均执行时间（使用指数加权移动平均）
        self.performance_stats['avg_execution_time_ms'] = (
            self.performance_stats['avg_execution_time_ms'] * 0.9 +
            execution_time_ms * 0.1
        )
        
        # 更新平均滑点
        if 'slippage' in result and 'current_price' in result:
            current_price = result['current_price']
            if current_price > 0:
                slippage_percent = result['slippage'] / current_price
                self.performance_stats['avg_slippage'] = (
                    self.performance_stats['avg_slippage'] * 0.9 +
                    slippage_percent * 0.1
                )
        
        # 更新平均市场冲击
        if 'market_impact' in result:
            self.performance_stats['avg_market_impact'] = (
                self.performance_stats['avg_market_impact'] * 0.9 +
                result['market_impact'] * 0.1
            )
    
    def get_execution_history(self, symbol: str = None, start_time: float = None, end_time: float = None) -> List[Dict[str, Any]]:
        """获取执行历史记录"""
        filtered_history = self.execution_history
        
        # 按股票代码过滤
        if symbol:
            filtered_history = [record for record in filtered_history 
                              if record['order_params'].get('symbol') == symbol]
        
        # 按时间范围过滤
        if start_time:
            filtered_history = [record for record in filtered_history if record['timestamp'] >= start_time]
        
        if end_time:
            filtered_history = [record for record in filtered_history if record['timestamp'] <= end_time]
        
        return filtered_history
    
    def get_performance_stats(self) -> Dict[str, Any]:
        """获取性能统计信息"""
        return self.performance_stats.copy()
    
    def set_strategy_parameters(self, **kwargs):
        """设置策略参数"""
        for key, value in kwargs.items():
            if key in self.config:
                self.config[key] = value
        
        logger.info(f"Updated strategy parameters: {kwargs}")
        return self.config
    
    def register_custom_strategy(self, strategy_type: ExecutionType, strategy_func: Callable):
        """注册自定义执行策略"""
        self.strategy_registry[strategy_type] = strategy_func
        self.performance_stats['strategy_usage_count'][strategy_type.name] = 0
        
        logger.info(f"Custom strategy registered: {strategy_type.name}")
        return True

# 示例使用
if __name__ == "__main__":
    import random
    
    # 创建ExecutionStrategy实例
    execution_strategy = ExecutionStrategy()
    
    # 准备订单参数
    order_params = {
        'symbol': 'AAPL',
        'qty': 100,
        'side': 'BUY',
        'current_price': 150.0,
        'limit_price': 150.5,
        'stop_price': 151.0
    }
    
    # 选择最优策略
    optimal_strategy = execution_strategy.select_optimal_strategy('AAPL', 100, 'MARKET', order_params)
    print(f"Optimal strategy: {optimal_strategy.name}")
    
    # 执行市价单
    market_result = execution_strategy.execute(ExecutionType.MARKET, order_params)
    print(f"Market order result: {market_result}")
    
    # 执行限价单
    limit_result = execution_strategy.execute(ExecutionType.LIMIT, order_params)
    print(f"Limit order result: {limit_result}")
    
    # 执行VWAP订单
    vwap_result = execution_strategy.execute(ExecutionType.VWAP, order_params)
    print(f"VWAP order result: {vwap_result}")
    
    # 获取性能统计
    stats = execution_strategy.get_performance_stats()
    print(f"Performance stats: {stats}")